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Abstract

STUDY QUESTION

Do weight management practices differ in women with and without PCOS?

SUMMARY ANSWER

Women in the general population with self-reported PCOS are more likely to be using healthy weight management practices and alternative non-lifestyle measures for weight management than women without PCOS.

WHAT IS KNOWN ALREADY

Lifestyle management is the first-line treatment in PCOS. However, the specific weight management practices used by women with PCOS and their effect on diet and physical activity are unclear.

STUDY DESIGN, SIZE, DURATION

The study was a population-based observational cross-sectional study involving women in the 1973–1978 cohort (n = 7767 total; n = 556 with PCOS, n = 7211 without PCOS).

PARTICIPANTS/MATERIALS, SETTING, METHODS

Women with and without self-reported PCOS were included. Self-reported outcome measures included healthy lifestyle-related or alternative non-lifestyle-related (e.g. laxatives or smoking) weight management practices, dietary intake and physical activity.

MAIN RESULTS AND THE ROLE OF CHANCE

Women with PCOS were more likely to be following both healthy [reducing meal or snack size (odds ratio (OR) 1.50, 95% CI 1.14, 1.96, P = 0.004) and reducing fat or sugar intake (OR 1.32, 95% CI 1.03, 1.69, P = 0.027) or following a low glycaemic index diet (OR 2.88, 95% CI 2.30, 3.59, P < 0.001)] and alternative [smoking (OR 1.60, 95% CI 1.02, 2.52, P = 0.043) or use of laxative, diet pills, fasting or diuretics (OR 1.45, 95% CI 1.07, 1.97, P = 0.017)] weight management practices than women without PCOS. In PCOS, the use of a range of healthy weight management practices was associated with increases in physical activity (P < 0.001), diet quality (P < 0.001), percentage protein intake (P < 0.001) and decreases in glycaemic index (P < 0.001), and percentages of fat (P = 0.001), saturated fat (P < 0.001) or fibre (P = 0.003). Use of alternative weight management practices was associated with decreases in diet quality.

LIMITATIONS, REASONS FOR CAUTION

Limitations include the use of self-reported data for PCOS, height, weight, diet, physical activity and weight management behaviours.

WIDER IMPLICATIONS OF THE FINDINGS

In PCOS, we should focus on improving healthy weight practices across both diet quality and quantity, and on assessing alternative weight practices and their potential adverse effect on dietary intake.

STUDY FUNDING/COMPETING INTEREST(S)

L.M. is supported by a South Australian Cardiovascular Research Development Program Fellowship (ID AC11S374); a program collaboratively funded by the National Heart Foundation, the South Australian Department of Health and the South Australian Health and Medical Research Institute. H.T. is supported by the NHMRC. S.A.M. is supported by an NHMRC Career Development Fellowship Level 2, ID1104636 and was previously supported by an ARC Future Fellowship (2011–2015, FT100100581). The authors declare no conflict of interest.

TRIAL REGISTRATION NUMBER

Not applicable

PCOS, overweight, diet, physical activity, weight management

Introduction

PCOS is a common endocrine condition affecting up to 18% of reproductive-aged women (March et al., 2010). It is associated with adverse reproductive, metabolic and psychological features (Moran et al., 2010a,b; Barry et al., 2011; Hart and Doherty, 2015). Insulin resistance is a key aetiological component of PCOS, intrinsically present even in lean women (Stepto et al., 2013) in a form mechanistically distinct from that associated with obesity. Weight gain subsequently contributes further to insulin resistance and worsens the features (Lim et al., 2013) and prevalence of PCOS (Teede et al., 2013). Weight management (prevention of weight gain, achieving modest weight loss and maintenance of reduced weight) is thus recommended in evidence-based guidelines as first-line treatment for PCOS (Teede et al., 2011). The relationship between excess weight and PCOS is further complicated by a potential bidirectional relationship, with PCOS associated with an increased prevalence of excess weight (Lim et al., 2012).

The adverse impact of excess weight and the potential predisposition to weight gain have led to intense interest in the optimal dietary approach both to improve the features of PCOS and to achieve optimal weight management. While the majority of clinicians recommend lifestyle management (Cussons et al., 2005; Sharma et al., 2010), women with PCOS report they rarely receive lifestyle advice (Gibson-Helm et al., 2014). Some women with PCOS may seek advice from a dietitian which ranges from standard population weight loss recommendations to tailored advice (Jeanes et al., 2009; Moran et al., 2009). However, a recent survey reported that only half of dietitians provided specific information on diet type (Sharma et al., 2010). Other research also highlights that women with PCOS feel dietary advice is broad and inadequate (Humphreys and Costarelli, 2008). In this context, women with PCOS may be more likely to seek information from both evidence-based and non-evidence-based sources such as books, peers, information resources or the internet (Humphreys and Costarelli, 2008; Jeanes et al., 2009). Non-evidence-based strategies (‘alternative’ strategies) can include fad diets, smoking, fasting or laxative use (Timperio et al., 2000; Hayes and Napolitano, 2012), which may be associated with inadequate dietary intake (Neumark-Sztainer et al., 1996, 2000). This is of particular concern in PCOS, where optimizing diet and physical activity are paramount for improving reproductive, metabolic and psychological health, independent of weight management (Teede et al., 2011).

There is limited literature examining weight management practices in PCOS and none in community-based women, or women not recruited from clinical populations, in comparison with women without PCOS or in association with diet or physical activity. The aim of this study was to examine weight management practices in a population sample of women with and without PCOS, and to relate these to diet and physical activity among women with PCOS.

Materials and Methods

Study population

This study is based on the Australian Longitudinal Study on Women's Health (ALSWH), a longitudinal population-based study of four age cohorts of Australian women. For this study, women were recruited from the young cohort born in 1973–1978 and aged 18–23 years at ALSWH commencement (in 1996). Women were randomly selected from the national health insurance scheme (Medicare) database with intentional over-sampling from rural and remote areas (Dobson et al., 2015). Further methodological details have been reported elsewhere (Brown et al., 1998; Lee, 2001). The Human Research Ethics Committees of the University of Newcastle, Australia and the University of Queensland, Australia approved the study and informed written consent was obtained from each participant.

This analysis comprised women at Survey 5 (in 2009) (aged 31–36 years), as PCOS status was only identified in Surveys 4 and 5 and food frequency questionnaire (FFQ) data were not collected in Survey 4. We analysed data from 7767 women who responded to the question on PCOS diagnosis in Survey 4 or 5 (‘In the last three years have you been diagnosed with or treated for polycystic ovary syndrome’) (n = 556 PCOS; n = 7211 non-PCOS) (Fig. 1). No specific inclusion or exclusion criteria were applied and all women were included, irrespective of pregnancy, medication or country of birth.
Flow diagram of participant inclusion in a study of weight management practices associated with PCOS.
Figure 1

Flow diagram of participant inclusion in a study of weight management practices associated with PCOS.

ALSWH, Australian Longitudinal Study on Women's Health.

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Anthropometric and demographic variables

Self-reported height, weight, BMI and waist circumference were reported, with overweight and obesity defined by the World Health Organization criteria (<18.5 kg/m2 for underweight, 18.5–<25 kg/m2 for healthy weight, 25–<30 kg/m2 for overweight, ≥30 kg/m2 for obesity). Demographic variables including parity, education, occupation and income were collected at Survey 5 and area of residence and country of birth were measured at Survey 1.

Dietary intake

Self-reported diet data were collected from the Dietary Questionnaire for Epidemiological Studies (DQES) Version 2, an FFQ developed by The Cancer Council of Victoria previously validated in young to middle-aged (16–48 years) Australian women in comparison with a 7-day weighed food record (Hodge et al., 2000). The dietary guidelines index (DGI) (McNaughton et al., 2009) was used to assess diet quality. The DGI reflects compliance with Dietary Guidelines for Australian adults and the Australian Guide to Healthy Eating and comprises dietary indicators for the consumption of five core food groups (vegetables, fruits, cereals, meat and alternatives and dairy) and discretionary foods (foods high in energy, saturated fat, added sugars, added salt or alcohol and low in fibre) and national recommendations for saturated fat and added sugars. Each component was scored on a scale of 0–10, with 10 indicating an optimal intake. The total score was the sum of 13 indicators, with the DGI having a possible range of 0–130 and a higher score indicating increased compliance with the dietary guidelines. Participants who had incomplete FFQ data (>10% of items with missing responses) or those who reported a daily energy intake of >14 700 kJ/day or <2100 kJ/day were excluded.

Physical activity

Data were collected on the frequency and duration of a variety of leisure-time and transport activities such as commuting including walking briskly, moderate-intensity leisure-time physical activity and vigorous-intensity leisure-time physical activity in the last week for activities lasting 10 min or more. Physical activity was calculated as the sum of the products of total weekly minutes in each of the three categories of physical activity and the metabolic equivalent value (MET) assigned to each category: [(walking minutes × 3.0) + (moderate-intensity physical activity minutes × 4.0) + (vigorous-intensity physical activity minutes × 7.5)]. Four ordered physical activity levels were created: 0–<40 MET·min/week was defined as nil/sedentary (Level 1); 40–<600 MET·min/week as low (Level 2); 600–<1200 MET·min/week as moderate (Level 3); and ≥1200 MET·min/week as high level of physical activity (Level 4). The moderate category (Level 3) is commensurate with the lower end of the range of physical activity recommended in the current Australian guidelines (at least 150 min per week moderate-intensity physical activity, or equivalent), and the high category threshold (Level 4) is commensurate with the upper end of the recommended range (300 min of moderate intensity per week, or equivalent) (Brown et al., 2012). Outliers were truncated at 28 h/week for total physical activity.

Weight management practices

Weight management practices were assessed as previously described (Madigan et al., 2015) (Table I). Participants were asked to indicate whether in the last 12 months they had used certain methods to control their weight or shape with responses of yes or no provided. Weight control practices were classified into two groups: healthy lifestyle-related practices [exercise, commercial weight loss programs, meal replacements or slimming products, reducing meal or snack size, cutting down on fats and/or sugars, following a low glycaemic index (GI) diet or following a diet book diet (e.g. Atkins, Zone, CSIRO diet, Liver Cleansing diet)] or non-lifestyle-related alternative practices (use of laxatives, diuretics or diet pills, fasting or smoking). Women were defined as following a healthy or alternative weight management practice if they followed any of the practices for that category.

Table I
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Questionnaire assessment of weight management behaviours.

Have you used any of these methods to lose weight or to control your weight or shape in the last 12 months?
Commercial weight loss programs (e.g. Weight Watchers®, Lite n’ Easy®, Sureslim®, Jenny Craig®)
Meal replacements or slimming products (e.g. OPTIFAST®, Herbalife®)
Exercise
Cut down on the size of meals or between meal snacks
Cut down on fats (low fat) and/or sugars
Low glycaemic index diet
Diet book diets (e.g. Atkins, Zone, CSIRO diet, Liver Cleansing diet)
Laxatives, diuretics or diet pills (e.g. orlistat or sibutramine)
Fasting
Smoking
Have you used any of these methods to lose weight or to control your weight or shape in the last 12 months?
Commercial weight loss programs (e.g. Weight Watchers®, Lite n’ Easy®, Sureslim®, Jenny Craig®)
Meal replacements or slimming products (e.g. OPTIFAST®, Herbalife®)
Exercise
Cut down on the size of meals or between meal snacks
Cut down on fats (low fat) and/or sugars
Low glycaemic index diet
Diet book diets (e.g. Atkins, Zone, CSIRO diet, Liver Cleansing diet)
Laxatives, diuretics or diet pills (e.g. orlistat or sibutramine)
Fasting
Smoking
Table I
Open in new tab

Questionnaire assessment of weight management behaviours.

Have you used any of these methods to lose weight or to control your weight or shape in the last 12 months?
Commercial weight loss programs (e.g. Weight Watchers®, Lite n’ Easy®, Sureslim®, Jenny Craig®)
Meal replacements or slimming products (e.g. OPTIFAST®, Herbalife®)
Exercise
Cut down on the size of meals or between meal snacks
Cut down on fats (low fat) and/or sugars
Low glycaemic index diet
Diet book diets (e.g. Atkins, Zone, CSIRO diet, Liver Cleansing diet)
Laxatives, diuretics or diet pills (e.g. orlistat or sibutramine)
Fasting
Smoking
Have you used any of these methods to lose weight or to control your weight or shape in the last 12 months?
Commercial weight loss programs (e.g. Weight Watchers®, Lite n’ Easy®, Sureslim®, Jenny Craig®)
Meal replacements or slimming products (e.g. OPTIFAST®, Herbalife®)
Exercise
Cut down on the size of meals or between meal snacks
Cut down on fats (low fat) and/or sugars
Low glycaemic index diet
Diet book diets (e.g. Atkins, Zone, CSIRO diet, Liver Cleansing diet)
Laxatives, diuretics or diet pills (e.g. orlistat or sibutramine)
Fasting
Smoking

Statistical analyses

Statistical analyses were performed using Stata version 13 (StataCorp, 146 College Station, TX, USA). Frequencies and descriptive statistics were expressed as n (%) and means (SEM), respectively. All reported P-values were two tailed, and P < 0.05 was considered as statistically significant for all analyses, with the exception of the association of weight management practices and dietary intake or physical activity. To allow for increased Type I error rates associated with multiple primary outcomes, a P-value of <0.005 was accepted as being significant based on Bonferroni adjustment (0.05/10 adjusting for 10 outcomes). Comparisons between women with and without PCOS were performed by independent Student's t-test (continuous variables) and chi-square test (categorical variables). Logistic regressions were used to assess the association between weight management practices and PCOS, and linear regression analyses were used to assess the association between weight management practices and diet or physical activity with adjustment for age, BMI, marital status, education, occupation, income and country of birth. Models were constructed to avoid collinearity and assessed for standard assumptions. Analyses were conducted using survey commands weighted by area of residence to adjust for deliberate over-sampling in rural and remote areas. The response rate for each outcome is listed in Supplementary Table SI.

Results

Participant characteristics

Participant characteristics are reported in Table II. As previously reported, women with PCOS had a higher BMI, weight and waist circumference, were younger and more likely to not have children than women without PCOS (Teede et al., 2013; Moran et al., 2015). Women with PCOS had higher energy intake, DGI and fibre intake and lower GI with no evidence for differences in macronutrient intake, glycaemic load, physical activity or the proportion meeting physical activity guideline recommendations, smoking or alcohol, compared with women without PCOS.

Table II
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Participant characteristics and health behaviours.

All§, n = 8200PCOS, n = 556Non-PCOS, n = 7211P
Characteristics
 Age (years)**33.7 ± 1.533.6 ± 1.433.7 ± 1.50.008
 BMI (kg/m2)**25.5 ± 5.928.2 ± 7.425.4 ± 5.7<0.001
BMI (WHO categories)<0.001
 Underweight205 (2.6)10 (1.9)190 (2.7)
 Healthy weight4019 (52.6)208 (38.9)3736 (53.5)
 Overweight1926 (25.2)122 (22.8)1776 (25.4)
 Obese1484 (19.4)195 (36.4)1277 (18.3)
Weight (kg)*70.6 ± 0.2077.4 ± 0.9770.1 ± 0.20<0.001
Waist circumference (cm)*86.0 ± 0.2090.7 ± 0.9085.6 ± 0.20<0.001
Personal income† (Australian $)0.459
 No income707 (9.7)54 (10.6)641 (9.7)
 Low (>$0–$36 399)2965 (40.8)200 (39.1)2725 (41.0)
 Medium ($36 400–$77 999)2556 (35.2)171 (33.5)2342 (35.3)
 High (>$78 000)1041 (14.3)86 (16.8)934 (14.1)
Highest qualification†0.633
 No formal qual./year 10/12 equiv.††1606 (20.8)103 (19.1)1475 (20.9)
 Trade/diploma2051 (26.5)142 (26.3)1879 (26.6)
 Degree or higher4077 (52.7)295 (54.6)3715 (52.6)
Occupation†0.901
 Professional3374 (43.5)247 (45.5)3063 (43.2)
 Associate professional1408 (18.2)94 (17.3)1299 (18.3)
 Clerical trade1336 (17.2)93 (17.1)1218 (17.1)
 No paid job1635 (21.1)109 (20.1)1505 (21.2)
Marital status†0.670
 Married4910 (62.4)349 (63.0)4481 (62.3)
 De facto1202 (15.3)78 (14.1)1101 (15.3)
 Separated/divorced405 (5.1)26 (4.7)374 (5.2)
 Widowed18 (0.02)0 (0)18 (0.03)
 Never married1339 (17.0)101 (18.2)1218 (16.9)
Number of children†0.042
 02863 (43.2)232 (41.7)2585 (35.8)
 11543 (20.2)113 (20.3)1398 (19.4)
 2–33196 (33.8)190 (34.2)2962 (41.1)
 ≥4294 (3.7)21 (3.8)266 (3.7)
Country of birth†0.318
 Oceania7433 (94.7)515 (93.1)6796 (94.8)
 Europe206 (2.6)18 (3.3)184 (2.6)
 African60 (0.8)7 (1.3)53 (0.7)
 Asian120 (1.5)11 (2.0)107 (1.5)
 Americas28 (0.4)2 (0.4)26 (0.4)
 Pregnant785 (10.1)62 (11.3)712 (10.0)0.365
Health behaviours
 Smoking status†0.502
  Never smoker4731 (60.0)330 (59.5)4321 (60.0)
  Ex-smoker2033 (25.8)152 (27.4)1854 (25.8)
  Smoke <10 cigarettes/day526 (6.7)32 (5.8)482 (6.7)
  Smoke 10–19 cigarettes/day378 (4.8)30 (5.4)341 (4.7)
  Smoke ≥20 cigarettes/day212 (2.7)11 (2.0)199 (2.8)
 Alcohol (g)*9.3 ± 0.168.2 ± 0.579.4 ± 0.170.068
 Energy (kJ)*6742.3 ± 27.37016.6 ± 111.16719.3 ± 28.30.009
 DGI*87.2 ± 0.1388.9 ± 0.5087.1 ± 0.14<0.001
 Glycaemic load*86.9 ± 0.4189.5 ± 1.686.7 ± 0.420.107
 Glycaemic index*59.6 ± 0.0550.2 ± 0.1850.7 ± 0.050.014
 Fibre (g)*19.2 ± 0.0920.4 ± 0.3719.1 ± 0.090.002
 Fat (%)*36.8 ± 0.0636.7 ± 0.2436.8 ± 0.060.673
 Saturated fat (%)*15.3 ± 0.0415.1 ± 0.1415.3 ± 0.040.210
 Protein (%)*20.9 ± 0.042.1 ± 0.1520.9 ± 0.040.230
 Carbohydrate (%)*40.5 ± 0.0740.4 ± 0.2840.5 ± 0.070.763
 Physical activity (METs·min/week)*822.2 ± 11.2786.0 ± 39.9825.0 ± 11.80.348
 Physical activity status0.704
  Sedentary physical activity1039 (13.6)78 (14.5)950 (13.6)
  Low physical activity3046 (39.8)217 (40.4)2775 (40.0)
  Moderate physical activity1703 (22.3)118 (22.0)1550 (22.2)
  High physical activity1864 (24.4)124 (23.1)1718 (24.6)
  Meeting physical activity guidelines3567 (46.6)242 (45.1)3268 (46.7)0.293
All§, n = 8200PCOS, n = 556Non-PCOS, n = 7211P
Characteristics
 Age (years)**33.7 ± 1.533.6 ± 1.433.7 ± 1.50.008
 BMI (kg/m2)**25.5 ± 5.928.2 ± 7.425.4 ± 5.7<0.001
BMI (WHO categories)<0.001
 Underweight205 (2.6)10 (1.9)190 (2.7)
 Healthy weight4019 (52.6)208 (38.9)3736 (53.5)
 Overweight1926 (25.2)122 (22.8)1776 (25.4)
 Obese1484 (19.4)195 (36.4)1277 (18.3)
Weight (kg)*70.6 ± 0.2077.4 ± 0.9770.1 ± 0.20<0.001
Waist circumference (cm)*86.0 ± 0.2090.7 ± 0.9085.6 ± 0.20<0.001
Personal income† (Australian $)0.459
 No income707 (9.7)54 (10.6)641 (9.7)
 Low (>$0–$36 399)2965 (40.8)200 (39.1)2725 (41.0)
 Medium ($36 400–$77 999)2556 (35.2)171 (33.5)2342 (35.3)
 High (>$78 000)1041 (14.3)86 (16.8)934 (14.1)
Highest qualification†0.633
 No formal qual./year 10/12 equiv.††1606 (20.8)103 (19.1)1475 (20.9)
 Trade/diploma2051 (26.5)142 (26.3)1879 (26.6)
 Degree or higher4077 (52.7)295 (54.6)3715 (52.6)
Occupation†0.901
 Professional3374 (43.5)247 (45.5)3063 (43.2)
 Associate professional1408 (18.2)94 (17.3)1299 (18.3)
 Clerical trade1336 (17.2)93 (17.1)1218 (17.1)
 No paid job1635 (21.1)109 (20.1)1505 (21.2)
Marital status†0.670
 Married4910 (62.4)349 (63.0)4481 (62.3)
 De facto1202 (15.3)78 (14.1)1101 (15.3)
 Separated/divorced405 (5.1)26 (4.7)374 (5.2)
 Widowed18 (0.02)0 (0)18 (0.03)
 Never married1339 (17.0)101 (18.2)1218 (16.9)
Number of children†0.042
 02863 (43.2)232 (41.7)2585 (35.8)
 11543 (20.2)113 (20.3)1398 (19.4)
 2–33196 (33.8)190 (34.2)2962 (41.1)
 ≥4294 (3.7)21 (3.8)266 (3.7)
Country of birth†0.318
 Oceania7433 (94.7)515 (93.1)6796 (94.8)
 Europe206 (2.6)18 (3.3)184 (2.6)
 African60 (0.8)7 (1.3)53 (0.7)
 Asian120 (1.5)11 (2.0)107 (1.5)
 Americas28 (0.4)2 (0.4)26 (0.4)
 Pregnant785 (10.1)62 (11.3)712 (10.0)0.365
Health behaviours
 Smoking status†0.502
  Never smoker4731 (60.0)330 (59.5)4321 (60.0)
  Ex-smoker2033 (25.8)152 (27.4)1854 (25.8)
  Smoke <10 cigarettes/day526 (6.7)32 (5.8)482 (6.7)
  Smoke 10–19 cigarettes/day378 (4.8)30 (5.4)341 (4.7)
  Smoke ≥20 cigarettes/day212 (2.7)11 (2.0)199 (2.8)
 Alcohol (g)*9.3 ± 0.168.2 ± 0.579.4 ± 0.170.068
 Energy (kJ)*6742.3 ± 27.37016.6 ± 111.16719.3 ± 28.30.009
 DGI*87.2 ± 0.1388.9 ± 0.5087.1 ± 0.14<0.001
 Glycaemic load*86.9 ± 0.4189.5 ± 1.686.7 ± 0.420.107
 Glycaemic index*59.6 ± 0.0550.2 ± 0.1850.7 ± 0.050.014
 Fibre (g)*19.2 ± 0.0920.4 ± 0.3719.1 ± 0.090.002
 Fat (%)*36.8 ± 0.0636.7 ± 0.2436.8 ± 0.060.673
 Saturated fat (%)*15.3 ± 0.0415.1 ± 0.1415.3 ± 0.040.210
 Protein (%)*20.9 ± 0.042.1 ± 0.1520.9 ± 0.040.230
 Carbohydrate (%)*40.5 ± 0.0740.4 ± 0.2840.5 ± 0.070.763
 Physical activity (METs·min/week)*822.2 ± 11.2786.0 ± 39.9825.0 ± 11.80.348
 Physical activity status0.704
  Sedentary physical activity1039 (13.6)78 (14.5)950 (13.6)
  Low physical activity3046 (39.8)217 (40.4)2775 (40.0)
  Moderate physical activity1703 (22.3)118 (22.0)1550 (22.2)
  High physical activity1864 (24.4)124 (23.1)1718 (24.6)
  Meeting physical activity guidelines3567 (46.6)242 (45.1)3268 (46.7)0.293

Data were analysed by independent Student's t-test for continuous variables and chi-square test for categorical variables. Bolded values represent significant associations.

DGI, dietary guidelines index; MET, metabolic equivalent values; WHO, World Health Organization.

§Relates to the entire cohort of women from Survey 5 of the Australian Longitudinal Study on Women's Health (ALSWH). Of these, 7767 were eligible for this specific substudy.

*Values represent mean (SEM).

**Values represent mean (SD). †Values represent n (%).

††Year 12 or equivalent (e.g. Higher School Certificate).

Table II
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Participant characteristics and health behaviours.

All§, n = 8200PCOS, n = 556Non-PCOS, n = 7211P
Characteristics
 Age (years)**33.7 ± 1.533.6 ± 1.433.7 ± 1.50.008
 BMI (kg/m2)**25.5 ± 5.928.2 ± 7.425.4 ± 5.7<0.001
BMI (WHO categories)<0.001
 Underweight205 (2.6)10 (1.9)190 (2.7)
 Healthy weight4019 (52.6)208 (38.9)3736 (53.5)
 Overweight1926 (25.2)122 (22.8)1776 (25.4)
 Obese1484 (19.4)195 (36.4)1277 (18.3)
Weight (kg)*70.6 ± 0.2077.4 ± 0.9770.1 ± 0.20<0.001
Waist circumference (cm)*86.0 ± 0.2090.7 ± 0.9085.6 ± 0.20<0.001
Personal income† (Australian $)0.459
 No income707 (9.7)54 (10.6)641 (9.7)
 Low (>$0–$36 399)2965 (40.8)200 (39.1)2725 (41.0)
 Medium ($36 400–$77 999)2556 (35.2)171 (33.5)2342 (35.3)
 High (>$78 000)1041 (14.3)86 (16.8)934 (14.1)
Highest qualification†0.633
 No formal qual./year 10/12 equiv.††1606 (20.8)103 (19.1)1475 (20.9)
 Trade/diploma2051 (26.5)142 (26.3)1879 (26.6)
 Degree or higher4077 (52.7)295 (54.6)3715 (52.6)
Occupation†0.901
 Professional3374 (43.5)247 (45.5)3063 (43.2)
 Associate professional1408 (18.2)94 (17.3)1299 (18.3)
 Clerical trade1336 (17.2)93 (17.1)1218 (17.1)
 No paid job1635 (21.1)109 (20.1)1505 (21.2)
Marital status†0.670
 Married4910 (62.4)349 (63.0)4481 (62.3)
 De facto1202 (15.3)78 (14.1)1101 (15.3)
 Separated/divorced405 (5.1)26 (4.7)374 (5.2)
 Widowed18 (0.02)0 (0)18 (0.03)
 Never married1339 (17.0)101 (18.2)1218 (16.9)
Number of children†0.042
 02863 (43.2)232 (41.7)2585 (35.8)
 11543 (20.2)113 (20.3)1398 (19.4)
 2–33196 (33.8)190 (34.2)2962 (41.1)
 ≥4294 (3.7)21 (3.8)266 (3.7)
Country of birth†0.318
 Oceania7433 (94.7)515 (93.1)6796 (94.8)
 Europe206 (2.6)18 (3.3)184 (2.6)
 African60 (0.8)7 (1.3)53 (0.7)
 Asian120 (1.5)11 (2.0)107 (1.5)
 Americas28 (0.4)2 (0.4)26 (0.4)
 Pregnant785 (10.1)62 (11.3)712 (10.0)0.365
Health behaviours
 Smoking status†0.502
  Never smoker4731 (60.0)330 (59.5)4321 (60.0)
  Ex-smoker2033 (25.8)152 (27.4)1854 (25.8)
  Smoke <10 cigarettes/day526 (6.7)32 (5.8)482 (6.7)
  Smoke 10–19 cigarettes/day378 (4.8)30 (5.4)341 (4.7)
  Smoke ≥20 cigarettes/day212 (2.7)11 (2.0)199 (2.8)
 Alcohol (g)*9.3 ± 0.168.2 ± 0.579.4 ± 0.170.068
 Energy (kJ)*6742.3 ± 27.37016.6 ± 111.16719.3 ± 28.30.009
 DGI*87.2 ± 0.1388.9 ± 0.5087.1 ± 0.14<0.001
 Glycaemic load*86.9 ± 0.4189.5 ± 1.686.7 ± 0.420.107
 Glycaemic index*59.6 ± 0.0550.2 ± 0.1850.7 ± 0.050.014
 Fibre (g)*19.2 ± 0.0920.4 ± 0.3719.1 ± 0.090.002
 Fat (%)*36.8 ± 0.0636.7 ± 0.2436.8 ± 0.060.673
 Saturated fat (%)*15.3 ± 0.0415.1 ± 0.1415.3 ± 0.040.210
 Protein (%)*20.9 ± 0.042.1 ± 0.1520.9 ± 0.040.230
 Carbohydrate (%)*40.5 ± 0.0740.4 ± 0.2840.5 ± 0.070.763
 Physical activity (METs·min/week)*822.2 ± 11.2786.0 ± 39.9825.0 ± 11.80.348
 Physical activity status0.704
  Sedentary physical activity1039 (13.6)78 (14.5)950 (13.6)
  Low physical activity3046 (39.8)217 (40.4)2775 (40.0)
  Moderate physical activity1703 (22.3)118 (22.0)1550 (22.2)
  High physical activity1864 (24.4)124 (23.1)1718 (24.6)
  Meeting physical activity guidelines3567 (46.6)242 (45.1)3268 (46.7)0.293
All§, n = 8200PCOS, n = 556Non-PCOS, n = 7211P
Characteristics
 Age (years)**33.7 ± 1.533.6 ± 1.433.7 ± 1.50.008
 BMI (kg/m2)**25.5 ± 5.928.2 ± 7.425.4 ± 5.7<0.001
BMI (WHO categories)<0.001
 Underweight205 (2.6)10 (1.9)190 (2.7)
 Healthy weight4019 (52.6)208 (38.9)3736 (53.5)
 Overweight1926 (25.2)122 (22.8)1776 (25.4)
 Obese1484 (19.4)195 (36.4)1277 (18.3)
Weight (kg)*70.6 ± 0.2077.4 ± 0.9770.1 ± 0.20<0.001
Waist circumference (cm)*86.0 ± 0.2090.7 ± 0.9085.6 ± 0.20<0.001
Personal income† (Australian $)0.459
 No income707 (9.7)54 (10.6)641 (9.7)
 Low (>$0–$36 399)2965 (40.8)200 (39.1)2725 (41.0)
 Medium ($36 400–$77 999)2556 (35.2)171 (33.5)2342 (35.3)
 High (>$78 000)1041 (14.3)86 (16.8)934 (14.1)
Highest qualification†0.633
 No formal qual./year 10/12 equiv.††1606 (20.8)103 (19.1)1475 (20.9)
 Trade/diploma2051 (26.5)142 (26.3)1879 (26.6)
 Degree or higher4077 (52.7)295 (54.6)3715 (52.6)
Occupation†0.901
 Professional3374 (43.5)247 (45.5)3063 (43.2)
 Associate professional1408 (18.2)94 (17.3)1299 (18.3)
 Clerical trade1336 (17.2)93 (17.1)1218 (17.1)
 No paid job1635 (21.1)109 (20.1)1505 (21.2)
Marital status†0.670
 Married4910 (62.4)349 (63.0)4481 (62.3)
 De facto1202 (15.3)78 (14.1)1101 (15.3)
 Separated/divorced405 (5.1)26 (4.7)374 (5.2)
 Widowed18 (0.02)0 (0)18 (0.03)
 Never married1339 (17.0)101 (18.2)1218 (16.9)
Number of children†0.042
 02863 (43.2)232 (41.7)2585 (35.8)
 11543 (20.2)113 (20.3)1398 (19.4)
 2–33196 (33.8)190 (34.2)2962 (41.1)
 ≥4294 (3.7)21 (3.8)266 (3.7)
Country of birth†0.318
 Oceania7433 (94.7)515 (93.1)6796 (94.8)
 Europe206 (2.6)18 (3.3)184 (2.6)
 African60 (0.8)7 (1.3)53 (0.7)
 Asian120 (1.5)11 (2.0)107 (1.5)
 Americas28 (0.4)2 (0.4)26 (0.4)
 Pregnant785 (10.1)62 (11.3)712 (10.0)0.365
Health behaviours
 Smoking status†0.502
  Never smoker4731 (60.0)330 (59.5)4321 (60.0)
  Ex-smoker2033 (25.8)152 (27.4)1854 (25.8)
  Smoke <10 cigarettes/day526 (6.7)32 (5.8)482 (6.7)
  Smoke 10–19 cigarettes/day378 (4.8)30 (5.4)341 (4.7)
  Smoke ≥20 cigarettes/day212 (2.7)11 (2.0)199 (2.8)
 Alcohol (g)*9.3 ± 0.168.2 ± 0.579.4 ± 0.170.068
 Energy (kJ)*6742.3 ± 27.37016.6 ± 111.16719.3 ± 28.30.009
 DGI*87.2 ± 0.1388.9 ± 0.5087.1 ± 0.14<0.001
 Glycaemic load*86.9 ± 0.4189.5 ± 1.686.7 ± 0.420.107
 Glycaemic index*59.6 ± 0.0550.2 ± 0.1850.7 ± 0.050.014
 Fibre (g)*19.2 ± 0.0920.4 ± 0.3719.1 ± 0.090.002
 Fat (%)*36.8 ± 0.0636.7 ± 0.2436.8 ± 0.060.673
 Saturated fat (%)*15.3 ± 0.0415.1 ± 0.1415.3 ± 0.040.210
 Protein (%)*20.9 ± 0.042.1 ± 0.1520.9 ± 0.040.230
 Carbohydrate (%)*40.5 ± 0.0740.4 ± 0.2840.5 ± 0.070.763
 Physical activity (METs·min/week)*822.2 ± 11.2786.0 ± 39.9825.0 ± 11.80.348
 Physical activity status0.704
  Sedentary physical activity1039 (13.6)78 (14.5)950 (13.6)
  Low physical activity3046 (39.8)217 (40.4)2775 (40.0)
  Moderate physical activity1703 (22.3)118 (22.0)1550 (22.2)
  High physical activity1864 (24.4)124 (23.1)1718 (24.6)
  Meeting physical activity guidelines3567 (46.6)242 (45.1)3268 (46.7)0.293

Data were analysed by independent Student's t-test for continuous variables and chi-square test for categorical variables. Bolded values represent significant associations.

DGI, dietary guidelines index; MET, metabolic equivalent values; WHO, World Health Organization.

§Relates to the entire cohort of women from Survey 5 of the Australian Longitudinal Study on Women's Health (ALSWH). Of these, 7767 were eligible for this specific substudy.

*Values represent mean (SEM).

**Values represent mean (SD). †Values represent n (%).

††Year 12 or equivalent (e.g. Higher School Certificate).

Weight management practices

Weight management practices followed by women with and without PCOS are reported in Table III. In unadjusted analyses, women with PCOS were more likely to be following the weight management practices of using meal replacements, reducing meal or snack size, reducing fat or sugar, following a low GI diet, following a diet book diet, using laxatives, diuretics or diet pills, smoking or following any healthy or alternative weight management practices. In adjusted analyses, these relationships were maintained for reducing meal or snack size, reducing fat or sugar, following a low GI diet, smoking and for the overall score for alternative practices.

Table III
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Weight management practices in women with and without PCOS.

OutcomePCOSNon-PCOSOR unadjustedOR adjustedaOR adjustedb
Exercise475/556 (85.4%)5901/7188 (82.1%)1.20 (0.93, 1.56) P = 0.1631.06 (0.81, 1.38) P = 0.6791.06 (0.79, 1.42) P = 0.717
Commercial weight loss programs94/555 (16.9%)1001/7202 (13.9%)1.19 (0.92, 1.53) P = 0.1820.91 (0.69, 1.19) P = 0.4720.91 (0.68, 1.21) P = 0.512
Meal replacements116/556 (20.9%)1067/7189 (14.8%)1.54 (1.22, 1.94)P< 0.0011.18 (0.92, 1.52) P = 0.1891.23 (0.93, 1.61) P = 0.142
Reduce meal or snack size447/554 (80.7%)5294/7199 (73.4%)1.60 (1.26, 2.02)P< 0.0011.34 (1.05, 1.71) P = 0.0181.50 (1.14, 1.96)P = 0.003
Reduce fat or sugar429/556 (77.2%)4952/7197 (68.8%)1.49 (1.20, 1.86)P < 0.0011.26 (1.01, 1.58) P = 0.0441.32 (1.03, 1.69)P = 0.027
Low GI diet187/555 (33.7%)980/7181 (13.6%)3.21 (2.62, 3.93)P < 0.0012.87 (2.33, 3.54) P < 0.0012.88 (2.30, 3.59)P < 0.001
Diet book diets64/555 (11.5%)593/7187 (8.3%)1.47 (1.10, 1.97)P = 0.0101.23 (0.91, 1.68) P = 0.1821.13 (0.80, 1.58) P = 0.490
Laxatives, diuretics or diet pills38/517 (6.8%)325/7195 (4.5%)1.65 (1.14, 2.38)P = 0.0081.33 (0.90, 1.95) P = 0.1521.39 (0.91, 2.11) P = 0.123
Fasting36/554 (6.5%)326/7195 (4.5%)1.44 (0.99, 2.11) P = 0.0581.35 (0.91, 2.00) P = 0.1381.48 (0.97, 2.27) P = 0.070
Smoking34/552 (6.2%)299/7193 (4.2%)1.59 (1.07, 2.36)P = 0.0211.39 (0.92, 2.09) P = 0.1161.60 (1.02, 2.52)P = 0.043
Any healthy practice519/556 (93.3%)6383/7207 (88.6%)1.65 (1.15, 2.37)P = 0.0061.32 (0.92, 1.91) P = 0.1351.34 (0.88, 2.03) P = 0.173
Any alternative practice77/555 (13.9%)718/7199 (10.0%)1.55 (1.19, 2.03)P = 0.0011.34 (1.02, 1.78) P = 0.0381.45 (1.07, 1.97)P = 0.017
OutcomePCOSNon-PCOSOR unadjustedOR adjustedaOR adjustedb
Exercise475/556 (85.4%)5901/7188 (82.1%)1.20 (0.93, 1.56) P = 0.1631.06 (0.81, 1.38) P = 0.6791.06 (0.79, 1.42) P = 0.717
Commercial weight loss programs94/555 (16.9%)1001/7202 (13.9%)1.19 (0.92, 1.53) P = 0.1820.91 (0.69, 1.19) P = 0.4720.91 (0.68, 1.21) P = 0.512
Meal replacements116/556 (20.9%)1067/7189 (14.8%)1.54 (1.22, 1.94)P< 0.0011.18 (0.92, 1.52) P = 0.1891.23 (0.93, 1.61) P = 0.142
Reduce meal or snack size447/554 (80.7%)5294/7199 (73.4%)1.60 (1.26, 2.02)P< 0.0011.34 (1.05, 1.71) P = 0.0181.50 (1.14, 1.96)P = 0.003
Reduce fat or sugar429/556 (77.2%)4952/7197 (68.8%)1.49 (1.20, 1.86)P < 0.0011.26 (1.01, 1.58) P = 0.0441.32 (1.03, 1.69)P = 0.027
Low GI diet187/555 (33.7%)980/7181 (13.6%)3.21 (2.62, 3.93)P < 0.0012.87 (2.33, 3.54) P < 0.0012.88 (2.30, 3.59)P < 0.001
Diet book diets64/555 (11.5%)593/7187 (8.3%)1.47 (1.10, 1.97)P = 0.0101.23 (0.91, 1.68) P = 0.1821.13 (0.80, 1.58) P = 0.490
Laxatives, diuretics or diet pills38/517 (6.8%)325/7195 (4.5%)1.65 (1.14, 2.38)P = 0.0081.33 (0.90, 1.95) P = 0.1521.39 (0.91, 2.11) P = 0.123
Fasting36/554 (6.5%)326/7195 (4.5%)1.44 (0.99, 2.11) P = 0.0581.35 (0.91, 2.00) P = 0.1381.48 (0.97, 2.27) P = 0.070
Smoking34/552 (6.2%)299/7193 (4.2%)1.59 (1.07, 2.36)P = 0.0211.39 (0.92, 2.09) P = 0.1161.60 (1.02, 2.52)P = 0.043
Any healthy practice519/556 (93.3%)6383/7207 (88.6%)1.65 (1.15, 2.37)P = 0.0061.32 (0.92, 1.91) P = 0.1351.34 (0.88, 2.03) P = 0.173
Any alternative practice77/555 (13.9%)718/7199 (10.0%)1.55 (1.19, 2.03)P = 0.0011.34 (1.02, 1.78) P = 0.0381.45 (1.07, 1.97)P = 0.017

Data are presented as odds ratio (OR) (95% CI) and were analysed using logistic regression analysis.

aAdjusted for age and BMI.

bAdjusted for age, BMI, marital status, education, occupation, income and country of birth.

Table III
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Weight management practices in women with and without PCOS.

OutcomePCOSNon-PCOSOR unadjustedOR adjustedaOR adjustedb
Exercise475/556 (85.4%)5901/7188 (82.1%)1.20 (0.93, 1.56) P = 0.1631.06 (0.81, 1.38) P = 0.6791.06 (0.79, 1.42) P = 0.717
Commercial weight loss programs94/555 (16.9%)1001/7202 (13.9%)1.19 (0.92, 1.53) P = 0.1820.91 (0.69, 1.19) P = 0.4720.91 (0.68, 1.21) P = 0.512
Meal replacements116/556 (20.9%)1067/7189 (14.8%)1.54 (1.22, 1.94)P< 0.0011.18 (0.92, 1.52) P = 0.1891.23 (0.93, 1.61) P = 0.142
Reduce meal or snack size447/554 (80.7%)5294/7199 (73.4%)1.60 (1.26, 2.02)P< 0.0011.34 (1.05, 1.71) P = 0.0181.50 (1.14, 1.96)P = 0.003
Reduce fat or sugar429/556 (77.2%)4952/7197 (68.8%)1.49 (1.20, 1.86)P < 0.0011.26 (1.01, 1.58) P = 0.0441.32 (1.03, 1.69)P = 0.027
Low GI diet187/555 (33.7%)980/7181 (13.6%)3.21 (2.62, 3.93)P < 0.0012.87 (2.33, 3.54) P < 0.0012.88 (2.30, 3.59)P < 0.001
Diet book diets64/555 (11.5%)593/7187 (8.3%)1.47 (1.10, 1.97)P = 0.0101.23 (0.91, 1.68) P = 0.1821.13 (0.80, 1.58) P = 0.490
Laxatives, diuretics or diet pills38/517 (6.8%)325/7195 (4.5%)1.65 (1.14, 2.38)P = 0.0081.33 (0.90, 1.95) P = 0.1521.39 (0.91, 2.11) P = 0.123
Fasting36/554 (6.5%)326/7195 (4.5%)1.44 (0.99, 2.11) P = 0.0581.35 (0.91, 2.00) P = 0.1381.48 (0.97, 2.27) P = 0.070
Smoking34/552 (6.2%)299/7193 (4.2%)1.59 (1.07, 2.36)P = 0.0211.39 (0.92, 2.09) P = 0.1161.60 (1.02, 2.52)P = 0.043
Any healthy practice519/556 (93.3%)6383/7207 (88.6%)1.65 (1.15, 2.37)P = 0.0061.32 (0.92, 1.91) P = 0.1351.34 (0.88, 2.03) P = 0.173
Any alternative practice77/555 (13.9%)718/7199 (10.0%)1.55 (1.19, 2.03)P = 0.0011.34 (1.02, 1.78) P = 0.0381.45 (1.07, 1.97)P = 0.017
OutcomePCOSNon-PCOSOR unadjustedOR adjustedaOR adjustedb
Exercise475/556 (85.4%)5901/7188 (82.1%)1.20 (0.93, 1.56) P = 0.1631.06 (0.81, 1.38) P = 0.6791.06 (0.79, 1.42) P = 0.717
Commercial weight loss programs94/555 (16.9%)1001/7202 (13.9%)1.19 (0.92, 1.53) P = 0.1820.91 (0.69, 1.19) P = 0.4720.91 (0.68, 1.21) P = 0.512
Meal replacements116/556 (20.9%)1067/7189 (14.8%)1.54 (1.22, 1.94)P< 0.0011.18 (0.92, 1.52) P = 0.1891.23 (0.93, 1.61) P = 0.142
Reduce meal or snack size447/554 (80.7%)5294/7199 (73.4%)1.60 (1.26, 2.02)P< 0.0011.34 (1.05, 1.71) P = 0.0181.50 (1.14, 1.96)P = 0.003
Reduce fat or sugar429/556 (77.2%)4952/7197 (68.8%)1.49 (1.20, 1.86)P < 0.0011.26 (1.01, 1.58) P = 0.0441.32 (1.03, 1.69)P = 0.027
Low GI diet187/555 (33.7%)980/7181 (13.6%)3.21 (2.62, 3.93)P < 0.0012.87 (2.33, 3.54) P < 0.0012.88 (2.30, 3.59)P < 0.001
Diet book diets64/555 (11.5%)593/7187 (8.3%)1.47 (1.10, 1.97)P = 0.0101.23 (0.91, 1.68) P = 0.1821.13 (0.80, 1.58) P = 0.490
Laxatives, diuretics or diet pills38/517 (6.8%)325/7195 (4.5%)1.65 (1.14, 2.38)P = 0.0081.33 (0.90, 1.95) P = 0.1521.39 (0.91, 2.11) P = 0.123
Fasting36/554 (6.5%)326/7195 (4.5%)1.44 (0.99, 2.11) P = 0.0581.35 (0.91, 2.00) P = 0.1381.48 (0.97, 2.27) P = 0.070
Smoking34/552 (6.2%)299/7193 (4.2%)1.59 (1.07, 2.36)P = 0.0211.39 (0.92, 2.09) P = 0.1161.60 (1.02, 2.52)P = 0.043
Any healthy practice519/556 (93.3%)6383/7207 (88.6%)1.65 (1.15, 2.37)P = 0.0061.32 (0.92, 1.91) P = 0.1351.34 (0.88, 2.03) P = 0.173
Any alternative practice77/555 (13.9%)718/7199 (10.0%)1.55 (1.19, 2.03)P = 0.0011.34 (1.02, 1.78) P = 0.0381.45 (1.07, 1.97)P = 0.017

Data are presented as odds ratio (OR) (95% CI) and were analysed using logistic regression analysis.

aAdjusted for age and BMI.

bAdjusted for age, BMI, marital status, education, occupation, income and country of birth.

Relationships of weight management status and dietary intake and physical activity

Relationships of weight management practices with specific diet and physical activity variables were examined only in women with PCOS (Table IV). For healthy weight management practices, meal replacements and reducing meal or snack size were associated with higher percentage protein; lower fat or sugar was associated with lower percentage fat and saturated fat, and higher percentage protein and physical activity; following a low GI diet was associated with higher DGI and lower GI, percentage fat and saturated fat; using diet books was associated with lower fibre and the combined score for use of any healthy weight management practice was associated with higher percentage protein. For alternative weight management practices, laxative use and the combined score for use of any practice was associated with lower DGI.

Table IV
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Differences in dietary intake and physical activity between women with PCOS who indicated they followed a specific weight management behaviour and those who indicated that they did not.

Energy intake (kJ)DGIGlycaemic loadGlycaemic indexFibre (grams)Fat (%)Saturated fat (%)Protein (%)CHO (%)Physical activity (METs·min/week)
Healthy practices
 Exercise−614.2 (−1301.3, 72.8)P = 0.0803.7 (0.22 7.2)P = 0.037−9.1 (−19.3, 1.2)P = 0.082−0.96 (−2.1, 0.14)P = 0.177−0.48 (−2.7, 1.7)P = 0.662−2.0 (−3.5, −0.45)P = 0.011−1.0 (−1.9, −0.15)P = 0.0221.6 (0.69, 2.4)P < 0.0010.36 (−1.4, 2.2)P = 0.690295.1 (90.7, 499.6)P = 0.005
 Commercial weight loss−136.9 (−828.0, 554.2)P = 0.6971.2 (−1.6, 4.0)P = 0.399−2.8 (−13.3, 7.6)P = 0.593−0.72 (−1.7, 0.3)P = 0.158−0.06 (−2.5, 2.4)P = 0.963−1.2 (−2.6, 0.2)P = 0.094−0.83 (−1.6, −0.02)P = 0.0451.3 (0.26, 2.3)P = 0.015−0.04 (−1.8, 1.7)P = 0.96116.7 (−195.3, 228.7)P = 0.877
 Meal replacements−365.8 (−950.9, 219.4)P = 0.220−1.9 (−4.8, 1.1)P = 0.210−10.6 (−19.3, −1.8)P = 0.018−0.57 (−1.5, 0.4)P = 0.235−1.9 (−4.0, 0.13)P = 0.0660.32 (−0.94, 1.6)P = 0.614−0.40 (−1.1, 0.33)P = 0.2851.9 (1.1, 2.8)P < 0.001−2.2 (−3.8, −0.53)P = 0.010142.8 (−84.7, 370.4)P = 0.218
 Reduce meal or snack size−640.0 (−1296.0, 16.0)P = 0.0563.1 (−0.2, 6.4)P = 0.066−10.1 (−19.5, −0.73)P = 0.035−0.92 (−1.9, 0.05)P = 0.063−1.2 (−3.2, 0.9)P = 0.273−1.5 (−3.0, −0.09)P = 0.037−0.92 (−1.8, −0.03)P = 0.0431.9 (1.1, 2.7)P < 0.001−0.33 (−2.0, 1.3)P = 0.700160.8 (−94.1, 415.7)P = 0.216
 Reduce fat or sugar−43.9 (−595.6, 507.8)P = 0.8763.3 (0.7, 6.0)P = 0.014−1.6 (−9.5, 6.2)P = 0.686−0.72 (−1.6, 0.19)P = 0.122−0.09 (−1.9, 1.7)P = 0.920−1.9 (−3.1, −0.76)P = 0.001−1.4 (−2.2, −0.70)P < 0.0011.7 (1.0, 2.5)P < 0.0010.16 (−1.1, 1.5)P = 0.810370.3 (186.9, 553.7)P< 0.001
 Low GI diet−217.0 (−707.1, 273.1)P = 0.3854.6 (2.3, 6.9)P < 0.001−3.3 (−10.4, 3.9)P = 0.372−2.2 (−2.9, −1.5)P < 0.0011.4 (−0.3, 3.2)P = 0.111−1.9 (−2.9, −0.80)P = 0.001−1.5 (−2.0, −0.87)P < 0.0010.83 (0.10, 1.6)P = 0.0250.99 (−0.33, 2.3) P = 0.142151.8 (−14.2, 317.8)P = 0.073
 Diet book diets−630.0 (−1412.9, 152.9)P = 0.114−1.1 (−4.3, 2.2)P = 0.523−12.7 (−23.7, −1.6)P = 0.025−0.98 (−2.2, 0.22)P = 0.110−3.1 (−5.1, −1.0)P = 0.0030.59 (−1.2, 2.4)P = 0.509−0.08 (−1.0, 0.85)P = 0.8661.1 (0.04, 2.2)P = 0.042−1.6 (−3.7, 0.56)P = 0.14783.6 (−162.5, 329.7)P = 0.505
 Any healthy practice−940.9 (−1872.5, −9.4)P = 0.0483.8 (−1.2, 8.8)P = 0.133−14.1 (−26.9, −1.2)P = 0.032−1.1 (−2.5, 0.28)P = 0.117−2.3 (−5.0, 0.46)P = 0.103−2.3 (−4.3, −0.31)P = 0.023−1.6 (−2.9, −0.37)P = 0.0122.3 (1.2, 3.5)P < 0.001−0.08 (−2.2, 2.0)P = 0.94277.6 (−238.9, 394.2)P = 0.630
Alternative practices
 Laxatives, diuretics or diet pills−116.4 (−1018.2, 785.5)P = 0.800−6.6 (−11.1, −2.1)P = 0.0040.11 (−14.5, 14.7)P = 0.9881.4 (0.12, 2.8)P = 0.033−3.0 (−5.6, −0.27)P = 0.0310.18 (−2.2, 2.6)P = 0.8820.49 (−0.82, 1.8)P = 0.4650.22 (−0.93, 1.4) P = 0.709−0.29 (−2.9, 2.3)P = 0.822−31.2 (−324.0, 261.6)P = 0.834
 Fasting37.8 (−871.0, 946.6)P = 0.935−3.6 (−7.8, 0.6)P = 0.0892.4 (−10.9, 15.7)P = 0.7241.18 (−0.55, 2.7)P = 0.194−0.94 (−3.7, 1.8)P = 0.503−0.79 (−3.1, 1.5)P = 0.506−0.32 (−1.5, 0.9)P = 0.5980.39 (−0.98, 1.8) P = 0.5780.45 (−2.1, 3.0)P = 0.722348.8 (−132.4, 830.0)P = 0.155
 Smoking−947.3 (−2070.8, 176.3)P = 0.098−4.5 (−9.6, 0.7)P = 0.093−12.1 (−28.8, 4.7)P = 0.1580.47 (−1.5, 2.4)P = 0.637−3.2 (−7.5, 1.1)P = 0.141−0.66 (−3.6, 2.3)P = 0.663−0.24 (−1.8, 1.4)P = 0.7731.3 (−0.27, 2.9) P = 0.103−0.49 (−3.8, 2.9)P = 0.77534.0 (−399.4, 467.5)P = 0.877
 Any alternative practice−357.4 (−1073.9, 359.1)P = 0.327−5.2 (−8.5, −1.8)P= 0.002−2.1 (−13.1, 8.9)P = 0.7071.2 (0.05, 2.4)P = 0.042−2.5 (−4.8, −0.09)P = 0.0420.22 (−1.9, 1.5)P = 0.7990.09 (−0.85, 1.0)P = 0.8510.40 (−0.66, 1.5) P = 0.457−0.10 (−2.1, 1.9)P = 0.918118.4 (−171.8, 408.6)P = 0.423
Energy intake (kJ)DGIGlycaemic loadGlycaemic indexFibre (grams)Fat (%)Saturated fat (%)Protein (%)CHO (%)Physical activity (METs·min/week)
Healthy practices
 Exercise−614.2 (−1301.3, 72.8)P = 0.0803.7 (0.22 7.2)P = 0.037−9.1 (−19.3, 1.2)P = 0.082−0.96 (−2.1, 0.14)P = 0.177−0.48 (−2.7, 1.7)P = 0.662−2.0 (−3.5, −0.45)P = 0.011−1.0 (−1.9, −0.15)P = 0.0221.6 (0.69, 2.4)P < 0.0010.36 (−1.4, 2.2)P = 0.690295.1 (90.7, 499.6)P = 0.005
 Commercial weight loss−136.9 (−828.0, 554.2)P = 0.6971.2 (−1.6, 4.0)P = 0.399−2.8 (−13.3, 7.6)P = 0.593−0.72 (−1.7, 0.3)P = 0.158−0.06 (−2.5, 2.4)P = 0.963−1.2 (−2.6, 0.2)P = 0.094−0.83 (−1.6, −0.02)P = 0.0451.3 (0.26, 2.3)P = 0.015−0.04 (−1.8, 1.7)P = 0.96116.7 (−195.3, 228.7)P = 0.877
 Meal replacements−365.8 (−950.9, 219.4)P = 0.220−1.9 (−4.8, 1.1)P = 0.210−10.6 (−19.3, −1.8)P = 0.018−0.57 (−1.5, 0.4)P = 0.235−1.9 (−4.0, 0.13)P = 0.0660.32 (−0.94, 1.6)P = 0.614−0.40 (−1.1, 0.33)P = 0.2851.9 (1.1, 2.8)P < 0.001−2.2 (−3.8, −0.53)P = 0.010142.8 (−84.7, 370.4)P = 0.218
 Reduce meal or snack size−640.0 (−1296.0, 16.0)P = 0.0563.1 (−0.2, 6.4)P = 0.066−10.1 (−19.5, −0.73)P = 0.035−0.92 (−1.9, 0.05)P = 0.063−1.2 (−3.2, 0.9)P = 0.273−1.5 (−3.0, −0.09)P = 0.037−0.92 (−1.8, −0.03)P = 0.0431.9 (1.1, 2.7)P < 0.001−0.33 (−2.0, 1.3)P = 0.700160.8 (−94.1, 415.7)P = 0.216
 Reduce fat or sugar−43.9 (−595.6, 507.8)P = 0.8763.3 (0.7, 6.0)P = 0.014−1.6 (−9.5, 6.2)P = 0.686−0.72 (−1.6, 0.19)P = 0.122−0.09 (−1.9, 1.7)P = 0.920−1.9 (−3.1, −0.76)P = 0.001−1.4 (−2.2, −0.70)P < 0.0011.7 (1.0, 2.5)P < 0.0010.16 (−1.1, 1.5)P = 0.810370.3 (186.9, 553.7)P< 0.001
 Low GI diet−217.0 (−707.1, 273.1)P = 0.3854.6 (2.3, 6.9)P < 0.001−3.3 (−10.4, 3.9)P = 0.372−2.2 (−2.9, −1.5)P < 0.0011.4 (−0.3, 3.2)P = 0.111−1.9 (−2.9, −0.80)P = 0.001−1.5 (−2.0, −0.87)P < 0.0010.83 (0.10, 1.6)P = 0.0250.99 (−0.33, 2.3) P = 0.142151.8 (−14.2, 317.8)P = 0.073
 Diet book diets−630.0 (−1412.9, 152.9)P = 0.114−1.1 (−4.3, 2.2)P = 0.523−12.7 (−23.7, −1.6)P = 0.025−0.98 (−2.2, 0.22)P = 0.110−3.1 (−5.1, −1.0)P = 0.0030.59 (−1.2, 2.4)P = 0.509−0.08 (−1.0, 0.85)P = 0.8661.1 (0.04, 2.2)P = 0.042−1.6 (−3.7, 0.56)P = 0.14783.6 (−162.5, 329.7)P = 0.505
 Any healthy practice−940.9 (−1872.5, −9.4)P = 0.0483.8 (−1.2, 8.8)P = 0.133−14.1 (−26.9, −1.2)P = 0.032−1.1 (−2.5, 0.28)P = 0.117−2.3 (−5.0, 0.46)P = 0.103−2.3 (−4.3, −0.31)P = 0.023−1.6 (−2.9, −0.37)P = 0.0122.3 (1.2, 3.5)P < 0.001−0.08 (−2.2, 2.0)P = 0.94277.6 (−238.9, 394.2)P = 0.630
Alternative practices
 Laxatives, diuretics or diet pills−116.4 (−1018.2, 785.5)P = 0.800−6.6 (−11.1, −2.1)P = 0.0040.11 (−14.5, 14.7)P = 0.9881.4 (0.12, 2.8)P = 0.033−3.0 (−5.6, −0.27)P = 0.0310.18 (−2.2, 2.6)P = 0.8820.49 (−0.82, 1.8)P = 0.4650.22 (−0.93, 1.4) P = 0.709−0.29 (−2.9, 2.3)P = 0.822−31.2 (−324.0, 261.6)P = 0.834
 Fasting37.8 (−871.0, 946.6)P = 0.935−3.6 (−7.8, 0.6)P = 0.0892.4 (−10.9, 15.7)P = 0.7241.18 (−0.55, 2.7)P = 0.194−0.94 (−3.7, 1.8)P = 0.503−0.79 (−3.1, 1.5)P = 0.506−0.32 (−1.5, 0.9)P = 0.5980.39 (−0.98, 1.8) P = 0.5780.45 (−2.1, 3.0)P = 0.722348.8 (−132.4, 830.0)P = 0.155
 Smoking−947.3 (−2070.8, 176.3)P = 0.098−4.5 (−9.6, 0.7)P = 0.093−12.1 (−28.8, 4.7)P = 0.1580.47 (−1.5, 2.4)P = 0.637−3.2 (−7.5, 1.1)P = 0.141−0.66 (−3.6, 2.3)P = 0.663−0.24 (−1.8, 1.4)P = 0.7731.3 (−0.27, 2.9) P = 0.103−0.49 (−3.8, 2.9)P = 0.77534.0 (−399.4, 467.5)P = 0.877
 Any alternative practice−357.4 (−1073.9, 359.1)P = 0.327−5.2 (−8.5, −1.8)P= 0.002−2.1 (−13.1, 8.9)P = 0.7071.2 (0.05, 2.4)P = 0.042−2.5 (−4.8, −0.09)P = 0.0420.22 (−1.9, 1.5)P = 0.7990.09 (−0.85, 1.0)P = 0.8510.40 (−0.66, 1.5) P = 0.457−0.10 (−2.1, 1.9)P = 0.918118.4 (−171.8, 408.6)P = 0.423

Data are presented as mean difference (95% CI) for non-followers of the weight management practices subtracted from the values of the weight management practices. Data were analysed using linear regression analysis adjusted for age, BMI, marital status, education, occupation, income and country of birth.

CHO, carbohydrate.

Table IV
Open in new tab

Differences in dietary intake and physical activity between women with PCOS who indicated they followed a specific weight management behaviour and those who indicated that they did not.

Energy intake (kJ)DGIGlycaemic loadGlycaemic indexFibre (grams)Fat (%)Saturated fat (%)Protein (%)CHO (%)Physical activity (METs·min/week)
Healthy practices
 Exercise−614.2 (−1301.3, 72.8)P = 0.0803.7 (0.22 7.2)P = 0.037−9.1 (−19.3, 1.2)P = 0.082−0.96 (−2.1, 0.14)P = 0.177−0.48 (−2.7, 1.7)P = 0.662−2.0 (−3.5, −0.45)P = 0.011−1.0 (−1.9, −0.15)P = 0.0221.6 (0.69, 2.4)P < 0.0010.36 (−1.4, 2.2)P = 0.690295.1 (90.7, 499.6)P = 0.005
 Commercial weight loss−136.9 (−828.0, 554.2)P = 0.6971.2 (−1.6, 4.0)P = 0.399−2.8 (−13.3, 7.6)P = 0.593−0.72 (−1.7, 0.3)P = 0.158−0.06 (−2.5, 2.4)P = 0.963−1.2 (−2.6, 0.2)P = 0.094−0.83 (−1.6, −0.02)P = 0.0451.3 (0.26, 2.3)P = 0.015−0.04 (−1.8, 1.7)P = 0.96116.7 (−195.3, 228.7)P = 0.877
 Meal replacements−365.8 (−950.9, 219.4)P = 0.220−1.9 (−4.8, 1.1)P = 0.210−10.6 (−19.3, −1.8)P = 0.018−0.57 (−1.5, 0.4)P = 0.235−1.9 (−4.0, 0.13)P = 0.0660.32 (−0.94, 1.6)P = 0.614−0.40 (−1.1, 0.33)P = 0.2851.9 (1.1, 2.8)P < 0.001−2.2 (−3.8, −0.53)P = 0.010142.8 (−84.7, 370.4)P = 0.218
 Reduce meal or snack size−640.0 (−1296.0, 16.0)P = 0.0563.1 (−0.2, 6.4)P = 0.066−10.1 (−19.5, −0.73)P = 0.035−0.92 (−1.9, 0.05)P = 0.063−1.2 (−3.2, 0.9)P = 0.273−1.5 (−3.0, −0.09)P = 0.037−0.92 (−1.8, −0.03)P = 0.0431.9 (1.1, 2.7)P < 0.001−0.33 (−2.0, 1.3)P = 0.700160.8 (−94.1, 415.7)P = 0.216
 Reduce fat or sugar−43.9 (−595.6, 507.8)P = 0.8763.3 (0.7, 6.0)P = 0.014−1.6 (−9.5, 6.2)P = 0.686−0.72 (−1.6, 0.19)P = 0.122−0.09 (−1.9, 1.7)P = 0.920−1.9 (−3.1, −0.76)P = 0.001−1.4 (−2.2, −0.70)P < 0.0011.7 (1.0, 2.5)P < 0.0010.16 (−1.1, 1.5)P = 0.810370.3 (186.9, 553.7)P< 0.001
 Low GI diet−217.0 (−707.1, 273.1)P = 0.3854.6 (2.3, 6.9)P < 0.001−3.3 (−10.4, 3.9)P = 0.372−2.2 (−2.9, −1.5)P < 0.0011.4 (−0.3, 3.2)P = 0.111−1.9 (−2.9, −0.80)P = 0.001−1.5 (−2.0, −0.87)P < 0.0010.83 (0.10, 1.6)P = 0.0250.99 (−0.33, 2.3) P = 0.142151.8 (−14.2, 317.8)P = 0.073
 Diet book diets−630.0 (−1412.9, 152.9)P = 0.114−1.1 (−4.3, 2.2)P = 0.523−12.7 (−23.7, −1.6)P = 0.025−0.98 (−2.2, 0.22)P = 0.110−3.1 (−5.1, −1.0)P = 0.0030.59 (−1.2, 2.4)P = 0.509−0.08 (−1.0, 0.85)P = 0.8661.1 (0.04, 2.2)P = 0.042−1.6 (−3.7, 0.56)P = 0.14783.6 (−162.5, 329.7)P = 0.505
 Any healthy practice−940.9 (−1872.5, −9.4)P = 0.0483.8 (−1.2, 8.8)P = 0.133−14.1 (−26.9, −1.2)P = 0.032−1.1 (−2.5, 0.28)P = 0.117−2.3 (−5.0, 0.46)P = 0.103−2.3 (−4.3, −0.31)P = 0.023−1.6 (−2.9, −0.37)P = 0.0122.3 (1.2, 3.5)P < 0.001−0.08 (−2.2, 2.0)P = 0.94277.6 (−238.9, 394.2)P = 0.630
Alternative practices
 Laxatives, diuretics or diet pills−116.4 (−1018.2, 785.5)P = 0.800−6.6 (−11.1, −2.1)P = 0.0040.11 (−14.5, 14.7)P = 0.9881.4 (0.12, 2.8)P = 0.033−3.0 (−5.6, −0.27)P = 0.0310.18 (−2.2, 2.6)P = 0.8820.49 (−0.82, 1.8)P = 0.4650.22 (−0.93, 1.4) P = 0.709−0.29 (−2.9, 2.3)P = 0.822−31.2 (−324.0, 261.6)P = 0.834
 Fasting37.8 (−871.0, 946.6)P = 0.935−3.6 (−7.8, 0.6)P = 0.0892.4 (−10.9, 15.7)P = 0.7241.18 (−0.55, 2.7)P = 0.194−0.94 (−3.7, 1.8)P = 0.503−0.79 (−3.1, 1.5)P = 0.506−0.32 (−1.5, 0.9)P = 0.5980.39 (−0.98, 1.8) P = 0.5780.45 (−2.1, 3.0)P = 0.722348.8 (−132.4, 830.0)P = 0.155
 Smoking−947.3 (−2070.8, 176.3)P = 0.098−4.5 (−9.6, 0.7)P = 0.093−12.1 (−28.8, 4.7)P = 0.1580.47 (−1.5, 2.4)P = 0.637−3.2 (−7.5, 1.1)P = 0.141−0.66 (−3.6, 2.3)P = 0.663−0.24 (−1.8, 1.4)P = 0.7731.3 (−0.27, 2.9) P = 0.103−0.49 (−3.8, 2.9)P = 0.77534.0 (−399.4, 467.5)P = 0.877
 Any alternative practice−357.4 (−1073.9, 359.1)P = 0.327−5.2 (−8.5, −1.8)P= 0.002−2.1 (−13.1, 8.9)P = 0.7071.2 (0.05, 2.4)P = 0.042−2.5 (−4.8, −0.09)P = 0.0420.22 (−1.9, 1.5)P = 0.7990.09 (−0.85, 1.0)P = 0.8510.40 (−0.66, 1.5) P = 0.457−0.10 (−2.1, 1.9)P = 0.918118.4 (−171.8, 408.6)P = 0.423
Energy intake (kJ)DGIGlycaemic loadGlycaemic indexFibre (grams)Fat (%)Saturated fat (%)Protein (%)CHO (%)Physical activity (METs·min/week)
Healthy practices
 Exercise−614.2 (−1301.3, 72.8)P = 0.0803.7 (0.22 7.2)P = 0.037−9.1 (−19.3, 1.2)P = 0.082−0.96 (−2.1, 0.14)P = 0.177−0.48 (−2.7, 1.7)P = 0.662−2.0 (−3.5, −0.45)P = 0.011−1.0 (−1.9, −0.15)P = 0.0221.6 (0.69, 2.4)P < 0.0010.36 (−1.4, 2.2)P = 0.690295.1 (90.7, 499.6)P = 0.005
 Commercial weight loss−136.9 (−828.0, 554.2)P = 0.6971.2 (−1.6, 4.0)P = 0.399−2.8 (−13.3, 7.6)P = 0.593−0.72 (−1.7, 0.3)P = 0.158−0.06 (−2.5, 2.4)P = 0.963−1.2 (−2.6, 0.2)P = 0.094−0.83 (−1.6, −0.02)P = 0.0451.3 (0.26, 2.3)P = 0.015−0.04 (−1.8, 1.7)P = 0.96116.7 (−195.3, 228.7)P = 0.877
 Meal replacements−365.8 (−950.9, 219.4)P = 0.220−1.9 (−4.8, 1.1)P = 0.210−10.6 (−19.3, −1.8)P = 0.018−0.57 (−1.5, 0.4)P = 0.235−1.9 (−4.0, 0.13)P = 0.0660.32 (−0.94, 1.6)P = 0.614−0.40 (−1.1, 0.33)P = 0.2851.9 (1.1, 2.8)P < 0.001−2.2 (−3.8, −0.53)P = 0.010142.8 (−84.7, 370.4)P = 0.218
 Reduce meal or snack size−640.0 (−1296.0, 16.0)P = 0.0563.1 (−0.2, 6.4)P = 0.066−10.1 (−19.5, −0.73)P = 0.035−0.92 (−1.9, 0.05)P = 0.063−1.2 (−3.2, 0.9)P = 0.273−1.5 (−3.0, −0.09)P = 0.037−0.92 (−1.8, −0.03)P = 0.0431.9 (1.1, 2.7)P < 0.001−0.33 (−2.0, 1.3)P = 0.700160.8 (−94.1, 415.7)P = 0.216
 Reduce fat or sugar−43.9 (−595.6, 507.8)P = 0.8763.3 (0.7, 6.0)P = 0.014−1.6 (−9.5, 6.2)P = 0.686−0.72 (−1.6, 0.19)P = 0.122−0.09 (−1.9, 1.7)P = 0.920−1.9 (−3.1, −0.76)P = 0.001−1.4 (−2.2, −0.70)P < 0.0011.7 (1.0, 2.5)P < 0.0010.16 (−1.1, 1.5)P = 0.810370.3 (186.9, 553.7)P< 0.001
 Low GI diet−217.0 (−707.1, 273.1)P = 0.3854.6 (2.3, 6.9)P < 0.001−3.3 (−10.4, 3.9)P = 0.372−2.2 (−2.9, −1.5)P < 0.0011.4 (−0.3, 3.2)P = 0.111−1.9 (−2.9, −0.80)P = 0.001−1.5 (−2.0, −0.87)P < 0.0010.83 (0.10, 1.6)P = 0.0250.99 (−0.33, 2.3) P = 0.142151.8 (−14.2, 317.8)P = 0.073
 Diet book diets−630.0 (−1412.9, 152.9)P = 0.114−1.1 (−4.3, 2.2)P = 0.523−12.7 (−23.7, −1.6)P = 0.025−0.98 (−2.2, 0.22)P = 0.110−3.1 (−5.1, −1.0)P = 0.0030.59 (−1.2, 2.4)P = 0.509−0.08 (−1.0, 0.85)P = 0.8661.1 (0.04, 2.2)P = 0.042−1.6 (−3.7, 0.56)P = 0.14783.6 (−162.5, 329.7)P = 0.505
 Any healthy practice−940.9 (−1872.5, −9.4)P = 0.0483.8 (−1.2, 8.8)P = 0.133−14.1 (−26.9, −1.2)P = 0.032−1.1 (−2.5, 0.28)P = 0.117−2.3 (−5.0, 0.46)P = 0.103−2.3 (−4.3, −0.31)P = 0.023−1.6 (−2.9, −0.37)P = 0.0122.3 (1.2, 3.5)P < 0.001−0.08 (−2.2, 2.0)P = 0.94277.6 (−238.9, 394.2)P = 0.630
Alternative practices
 Laxatives, diuretics or diet pills−116.4 (−1018.2, 785.5)P = 0.800−6.6 (−11.1, −2.1)P = 0.0040.11 (−14.5, 14.7)P = 0.9881.4 (0.12, 2.8)P = 0.033−3.0 (−5.6, −0.27)P = 0.0310.18 (−2.2, 2.6)P = 0.8820.49 (−0.82, 1.8)P = 0.4650.22 (−0.93, 1.4) P = 0.709−0.29 (−2.9, 2.3)P = 0.822−31.2 (−324.0, 261.6)P = 0.834
 Fasting37.8 (−871.0, 946.6)P = 0.935−3.6 (−7.8, 0.6)P = 0.0892.4 (−10.9, 15.7)P = 0.7241.18 (−0.55, 2.7)P = 0.194−0.94 (−3.7, 1.8)P = 0.503−0.79 (−3.1, 1.5)P = 0.506−0.32 (−1.5, 0.9)P = 0.5980.39 (−0.98, 1.8) P = 0.5780.45 (−2.1, 3.0)P = 0.722348.8 (−132.4, 830.0)P = 0.155
 Smoking−947.3 (−2070.8, 176.3)P = 0.098−4.5 (−9.6, 0.7)P = 0.093−12.1 (−28.8, 4.7)P = 0.1580.47 (−1.5, 2.4)P = 0.637−3.2 (−7.5, 1.1)P = 0.141−0.66 (−3.6, 2.3)P = 0.663−0.24 (−1.8, 1.4)P = 0.7731.3 (−0.27, 2.9) P = 0.103−0.49 (−3.8, 2.9)P = 0.77534.0 (−399.4, 467.5)P = 0.877
 Any alternative practice−357.4 (−1073.9, 359.1)P = 0.327−5.2 (−8.5, −1.8)P= 0.002−2.1 (−13.1, 8.9)P = 0.7071.2 (0.05, 2.4)P = 0.042−2.5 (−4.8, −0.09)P = 0.0420.22 (−1.9, 1.5)P = 0.7990.09 (−0.85, 1.0)P = 0.8510.40 (−0.66, 1.5) P = 0.457−0.10 (−2.1, 1.9)P = 0.918118.4 (−171.8, 408.6)P = 0.423

Data are presented as mean difference (95% CI) for non-followers of the weight management practices subtracted from the values of the weight management practices. Data were analysed using linear regression analysis adjusted for age, BMI, marital status, education, occupation, income and country of birth.

CHO, carbohydrate.

Discussion

We report here a comprehensive examination of weight management practices in community samples of women with and without PCOS. Women with PCOS were more likely to follow a number of both healthy weight management practices and alternative ones. Those following healthy practices had improved dietary intake with regard to a number of domains of diet quality while those following alternative weight management practices (specifically laxative use) had a poorer quality of diet.

While the women with PCOS had higher adiposity (Moran et al., 2013,b, 2015; Teede et al., 2013), the greater engagement in weight management practices observed here was maintained on adjustment for BMI and other demographic factors for some, but not all practices. This indicates that PCOS status itself is associated with a higher prevalence of engaging in weight management practices. This may be related to women with PCOS being more likely to perceive themselves at risk of obesity (Moran et al., 2010a) and primary health care physicians being more likely to diagnose overweight and obesity in patients with associated comorbidities (Battaglia et al., 2011; Ossolinski et al., 2015). These findings are consistent with difficulty in losing weight being the most common health concern for n = 1385 women with PCOS (Gibson-Helm et al., 2016).

Women with PCOS were more likely to report following specific healthy weight management approaches such as reducing meal or snack size or fat or sugar intake, or a low GI diet, compared with women without PCOS. These approaches are consistent with weight management guidelines (Jensen et al., 2014) and recommended by specialists in primary health care (Ossolinski et al., 2015) and in the limited literature on PCOS (Humphreys and Costarelli, 2008). Of interest, following a low GI diet was the strategy with the greatest difference in uptake between women with and without PCOS. The prevalence of following a low GI diet for weight management for non-PCOS women here (13.6%) is similar to a New Zealand community-based study of older women (11.2%) (Leong et al., 2013). The reason for a higher prevalence in PCOS (33.7%) may relate to its perceived importance in PCOS. A low GI diet resulted in greater improvements in insulin resistance, menstrual regularity and fibrinogen in PCOS (Marsh et al., 2010; Barr et al., 2013) and in anthropometry, lipids, inflammation and insulin in a meta-analysis in the general population (Thomas et al., 2007; Goff et al., 2013; Schwingshackl and Hoffmann, 2013) although a recent systematic review reported insufficient evidence to recommend one specific dietary approach as being beneficial in PCOS (Moran et al., 2013a). However, despite being willing to follow a healthy approach generally consistent with population weight management guidelines, women with PCOS still have a greater rate of weight gain related to higher caloric intake (Moran et al., 2013,b).

We report here that women with and without PCOS had a similar uptake of exercise for weight management, consistent with previous findings (Wright et al., 2004) of no differences in physical activity between women with and without PCOS. The overall prevalence of undertaking physical activity was high for both women with (85.4%) or without (82.1%) PCOS and comparable to Australia community-based data (84.4%) (Timperio et al., 2000). Exercise in PCOS reduces insulin resistance and has health benefits independent of weight change (Hutchison et al., 2011; Moran et al., 2011), and evidence-based guidelines for PCOS management recommend incorporating structured physical activity advice (Teede et al., 2011). As this study was conducted prior to the PCOS guidelines release (2009 versus 2011), women may therefore have been less likely to receive this advice. Young women also have a preference for dietary versus physical activity-based approaches for weight management (Lombard et al., 2010) and barriers for increasing physical activity are even more pronounced in PCOS (Banting et al., 2014). This highlights the need to address barriers to encourage exercise participation.

Women with PCOS were also more likely to follow alternative non-lifestyle-related weight management practices than women without PCOS. In the general population, the use of unhealthy weight control practices is a potential risk factors for the future development of eating disorders, depression or weight cycling (Ferraro et al., 2015). This is consistent with the greater likelihood that women with PCOS will exhibit disordered eating patterns such as bulimia nervosa (Naessen et al., 2006), worsened psychological health (Barry et al., 2011) and greater weight gain (Teede et al., 2013) than women without PCOS. Importantly, this highlights the clinical need for women with PCOS to receive education on the appropriate use of healthy weight control practices, and assessment and management of the use of alternative weight control practices.

For women with PCOS, following healthy weight management practices, including following a low GI diet, reducing fat or sugar intake or reducing meal or snack size, was associated with improved diet. The greatest number of dietary changes occurred for a low GI diet, which was associated with higher diet quality and lower GI, and percentage fat and saturated fat. Overall, these are positive dietary modifications consistent with population dietary or weight management recommendations (Jensen et al., 2014; Ossolinski et al., 2015) and indicate a potentially beneficial effect of following these strategies. This is consistent with the association of the use of healthy weight management strategies such as choosing lower-kilojoule foods with improved diet quality (Lin et al., 2013) and lower percentage fat and higher percentage protein (Neumark-Sztainer et al., 2000). In relation to alternative weight management practices, the only change observed here was worsened diet quality predominantly related to laxative use. This is consistent with prior reports in the general population where those following unhealthy weight management practices had a higher proportion of energy intake from fat or sweets and a lower fibre, fruit and iron intake (Neumark-Sztainer et al., 1996, 2000) than those following healthy weight management practices. The clinical relevance of the higher (4.6 units) or lower (6.6 units) DGI for a low GI diet or laxative use, respectively, is unclear, given that a 10 unit greater DGI is associated with only subtle metabolic improvements (McNaughton et al., 2009). However, the potential detrimental impact of unhealthy weight management practices on nutritional intake should still be considered for women with PCOS. This affirms that interpretation of weight management strategies in PCOS should focus on changes in dietary quantity as well as quality.

Strengths of this study include the use of a large community-based cohort, rather than women recruited from fertility or endocrinology clinics (Humphreys and Costarelli, 2008), who are more likely to be more overweight (Ezeh et al., 2013) and more adversely affected. Limitations include the use of self-reported data for height, weight, diet, physical activity and weight management behaviours. While the use of self-report of PCOS is also a limitation, it is not feasible to clinically verify PCOS or control status in population-based research. However, we previously validated self-reported PCOS status with menstrual cyclicity in this cohort (Teede et al., 2013). We note categories for defining weight management practices and our post hoc definitions of healthy or alternative practices are broad and may contain some degree of overlap. For example, some [e.g. the CSIRO diet (Noakes et al., 2005)], but not all, diet book approaches are evidence based. ‘Diet pills’ may also include medically prescribed pharmacological agents (e.g. orlistat or sibutramine) in addition to unhealthy weight management medications (e.g. laxatives).

In conclusion, we report the independent association of PCOS status with healthy and alternative weight management practices and the independent association of healthy weight management practices with better dietary quality and alternative weight management practices with worsened dietary quality in PCOS. Future weight management advice for women with PCOS needs to consider assessment and treatment of both healthy and unhealthy weight management practices. There is also a need to focus both on improving diet quality and on reducing dietary energy intake when optimizing dietary intake for weight management.

Supplementary data

Supplementary data are available at Human Reproduction online.

Acknowledgements

The research on which this paper is based was conducted as part of the Australian Longitudinal Study on Women's Health, which was conceived and developed by groups of inter-disciplinary researchers at The University of Newcastle and The University of Queensland. We are grateful to the women who provided the survey data. The authors thank Professor Graham Giles of the Cancer Epidemiology Centre of The Cancer Council Victoria for permission to use the Dietary Questionnaire for Epidemiological Studies (version 2), Melbourne: The Cancer Council Victoria, 1996. We also thank all the participants for their valuable contribution to this project.

Authors’ roles

L.M. and W.B. had substantial contributions to conception and design, or acquisition of data. L.M., A.J. and S.M. contributed to analysis of data. L.M., W.B., A.J., S.M. and H.T. contributed to interpretation of data, drafting the article or revising it critically for important intellectual content and had final approval of the version to be published.

Funding

We are grateful to the Australian Government Department of Health for funding. L.M. is supported by a South Australian Cardiovascular Research Development Program Fellowship (ID AC11S374); a program collaboratively funded by the National Heart Foundation, the South Australian Department of Health and the South Australian Health and Medical Research Institute. H.T. is supported by the NHMRC. S.A.M. is supported by an NHMRC Career Development Fellowship Level 2, ID1104636 and was previously supported by an ARC Future Fellowship (2011–2015, FT100100581).

Conflict of interest

None declared.

References

Banting
LK
,
Gibson-Helm
M
,
Polman
R
,
Teede
HJ
,
Stepto
NK
.
Physical activity and mental health in women with polycystic ovary syndrome
.
BMC Womens Health
2014
;
14
:
51
.

Google Scholar

Crossref
Search ADS
PubMed

 

Barr
S
,
Reeves
S
,
Sharp
K
,
Jeanes
YM
.
An isocaloric low glycemic index diet improves insulin sensitivity in women with polycystic ovary syndrome
.
J Acad Nutr Diet
2013
;
113
:
1523
–
1531
.

Google Scholar

Crossref
Search ADS
PubMed

 

Barry
JA
,
Kuczmierczyk
AR
,
Hardiman
PJ
.
Anxiety and depression in polycystic ovary syndrome: a systematic review and meta-analysis
.
Hum Reprod
2011
;
26
:
2442
–
2451
.

Google Scholar

Crossref
Search ADS
PubMed

 

Battaglia
C
,
Battaglia
B
,
Mancini
F
,
Paradisi
R
,
Fabbri
R
,
Venturoli
S
.
Ultrasonographic extended-view technique for evaluation of abdominal fat distribution in lean women with polycystic ovary syndrome
.
Acta Obstet Gynecol Scand
2011
;
90
:
600
–
608
.

Google Scholar

Crossref
Search ADS
PubMed

 

Brown
WJ
,
Bauman
AE
,
Bull
FC
,
Burton
NW
. Development of evidence-based physical activity recommendations for adults (18–64 years). Report Prepared for the Australian Government Department of Health.
2012
. Department of Health, Canberra.

Brown
WJ
,
Bryson
L
,
Byles
JE
,
Dobson
AJ
,
Lee
C
,
Mishra
G
,
Schofield
M
.
Women's Health Australia: recruitment for a national longitudinal cohort study
.
Women Health
1998
;
28
:
23
–
40
.

Google Scholar

Crossref
Search ADS
PubMed

 

Cussons
AJ
,
Stuckey
BG
,
Walsh
JP
,
Burke
V
,
Norman
RJ
.
Polycystic ovarian syndrome: marked differences between endocrinologists and gynaecologists in diagnosis and management
.
Clin Endocrinol
2005
;
62
:
289
–
295
.

Google Scholar

Crossref
Search ADS

 

Dobson
AJ
,
Hockey
R
,
Brown
WJ
,
Byles
JE
,
Loxton
DJ
,
McLaughlin
D
,
Tooth
LR
,
Mishra
GD
.
Cohort profile update: Australian Longitudinal Study on Women's Health
.
Int J Epidemiol
2015
;
44
:
1547a
–
1547f
.

Google Scholar

Crossref
Search ADS

 

Ezeh
U
,
Yildiz
BO
,
Azziz
R
.
Referral bias in defining the phenotype and prevalence of obesity in polycystic ovary syndrome
.
J Clin Endocrinol Metab
2013
;
98
:
E1088
–
96
.

Google Scholar

Crossref
Search ADS
PubMed

 

Ferraro
ZM
,
Patterson
S
,
Chaput
JP
.
Unhealthy weight control practices: culprits and clinical recommendations
.
Clin Med Insights Endocrinol Diabetes
2015
;
8
:
7
–
11
.

Google Scholar

Crossref
Search ADS
PubMed

 

Gibson-Helm
M
,
Teede
H
,
Dunaif
A
,
Dokras
A
.
Delayed diagnosis and a lack of information associated with dissatisfaction in women with polycystic ovary syndrome
.
J Clin Endocrinol Metab
2016
. In press.

Google Scholar

OpenURL Placeholder Text

 

Gibson-Helm
ME
,
Lucas
IM
,
Boyle
JA
,
Teede
HJ
.
Women's experiences of polycystic ovary syndrome diagnosis
.
Fam Pract
2014
;
31
:
545
–
549
.

Google Scholar

Crossref
Search ADS
PubMed

 

Goff
LM
,
Cowland
DE
,
Hooper
L
,
Frost
GS
.
Low glycaemic index diets and blood lipids: a systematic review and meta-analysis of randomised controlled trials
.
Nutr Metab Cardiovasc Dis
2013
;
23
:
1
–
10
.

Google Scholar

Crossref
Search ADS
PubMed

 

Hart
R
,
Doherty
DA
.
The potential implications of a PCOS diagnosis on a woman's long-term health using data linkage
.
J Clin Endocrinol Metab
2015
;
100
:
911
–
919
.

Google Scholar

Crossref
Search ADS
PubMed

 

Hayes
S
,
Napolitano
MA
.
Examination of weight control practices in a non-clinical sample of college women
.
Eat Weight Disord
2012
;
17
:
e157
–
163
.

Google Scholar

Crossref
Search ADS
PubMed

 

Hodge
A
,
Patterson
AJ
,
Brown
WJ
,
Ireland
P
,
Giles
G
.
The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation
.
Aust NZ J Public Health
2000
;
24
:
576
–
583
.

Google Scholar

Crossref
Search ADS

 

Humphreys
L
,
Costarelli
V
.
Implementation of dietary and general lifestyle advice among women with polycystic ovarian syndrome
.
J R Soc Health
2008
;
128
:
190
–
195
.

Google Scholar

Crossref
Search ADS

 

Hutchison
SK
,
Stepto
NK
,
Harrison
CL
,
Moran
LJ
,
Strauss
BJ
,
Teede
HJ
.
Effects of exercise on insulin resistance and body composition in overweight and obese women with and without polycystic ovary syndrome
.
J Clin Endocrinol Metab
2011
;
96
:
E48
–
56
.

Google Scholar

Crossref
Search ADS
PubMed

 

Jeanes
YM
,
Barr
S
,
Smith
K
,
Hart
KH
.
Dietary management of women with polycystic ovary syndrome in the United Kingdom: the role of dietitians
.
J Hum Nutr Diet
2009
;
22
:
551
–
558
.

Google Scholar

Crossref
Search ADS
PubMed

 

Jensen
MD
,
Ryan
DH
,
Apovian
CM
,
Loria
CM
,
Ard
JD
,
Millen
BE
,
Comuzzie
AG
,
Nonas
CA
,
Donato
KA
,
Pi-Sunyer
FX
et al. .
2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults: A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and The Obesity Society
.
J Am Coll Cardiol
2014
;
63
:
2985
–
3023
.

Google Scholar

Crossref
Search ADS
PubMed

 

Lee
C
.
Women's Health Australia: Progress on the Australian Longitudinal Study on Women's Health 1995–2000
.
Brisbane
:
Australian Academic Press Pty Ltd
,
2001
.

Google Scholar

OpenURL Placeholder Text

 

Leong
SL
,
Madden
C
,
Gray
AR
,
Horwath
CC
.
A nationwide survey of weight control practices among middle-aged New Zealand women
.
NZ Med J
2013
;
126
:
12
–
20
.

Google Scholar

OpenURL Placeholder Text

 

Lim
SS
,
Davies
MJ
,
Norman
RJ
,
Moran
LJ
.
Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and meta-analysis
.
Hum Reprod Update
2012
;
18
:
618
–
637
.

Google Scholar

Crossref
Search ADS
PubMed

 

Lim
SS
,
Norman
RJ
,
Davies
MJ
,
Moran
LJ
.
The effect of obesity on polycystic ovary syndrome: a systematic review and meta-analysis
.
Obes Rev
2013
;
14
:
95
–
109
.

Google Scholar

Crossref
Search ADS
PubMed

 

Lin
CT
,
Gao
Z
,
Lee
JY
.
Associations between self-reported weight management methods with diet quality as measured by the Healthy Eating Index-2005
.
Prev Med
2013
;
57
:
238
–
243
.

Google Scholar

Crossref
Search ADS
PubMed

 

Lombard
C
,
Deeks
A
,
Jolley
D
,
Ball
K
,
Teede
H
.
A low intensity, community based lifestyle programme to prevent weight gain in women with young children: cluster randomised controlled trial
.
BMJ
2010
;
341
:
c3215
.

Google Scholar

Crossref
Search ADS
PubMed

 

Madigan
CD
,
Daley
AJ
,
Kabir
E
,
Aveyard
P
,
Brown
W
.
Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis
.
Int J Obes (Lond)
2015
;
39
:
1601
–
1606
.

Google Scholar

Crossref
Search ADS
PubMed

 

March
WA
,
Moore
VM
,
Willson
KJ
,
Phillips
DI
,
Norman
RJ
,
Davies
MJ
.
The prevalence of polycystic ovary syndrome in a community sample assessed under contrasting diagnostic criteria
.
Hum Reprod
2010
;
25
:
544
–
551
.

Google Scholar

Crossref
Search ADS
PubMed

 

Marsh
KA
,
Steinbeck
KS
,
Atkinson
FS
,
Petocz
P
,
Brand-Miller
JC
.
Effect of a low glycemic index compared with a conventional healthy diet on polycystic ovary syndrome
.
Am J Clin Nutr
2010
;
92
:
83
–
92
.

Google Scholar

Crossref
Search ADS
PubMed

 

McNaughton
SA
,
Dunstan
DW
,
Ball
K
,
Shaw
J
,
Crawford
D
.
Dietary quality is associated with diabetes and cardio-metabolic risk factors
.
J Nutr
2009
;
139
:
734
–
742
.

Google Scholar

Crossref
Search ADS
PubMed

 

Moran
L
,
Gibson-Helm
M
,
Teede
H
,
Deeks
A
.
Polycystic ovary syndrome: a biopsychosocial understanding in young women to improve knowledge and treatment options
.
J Psychosom Obstet Gynaecol
2010
a;
31
:
24
–
31
.

Google Scholar

Crossref
Search ADS
PubMed

 

Moran
LJ
,
Grieger
JA
,
Mishra
GD
,
Teede
HJ
.
The association of a mediterranean-style diet pattern with polycystic ovary syndrome status in a community cohort study
.
Nutrients
2015
;
7
:
8553
–
8564
.

Google Scholar

Crossref
Search ADS
PubMed

 

Moran
LJ
,
Harrison
CL
,
Hutchison
SK
,
Stepto
NK
,
Strauss
BJ
,
Teede
HJ
.
Exercise decreases anti-mullerian hormone in anovulatory overweight women with polycystic ovary syndrome: a pilot study
.
Horm Metab Res
2011
;
43
:
977
–
979
.

Google Scholar

Crossref
Search ADS
PubMed

 

Moran
LJ
,
Ko
H
,
Misso
M
,
Marsh
K
,
Noakes
M
,
Talbot
M
,
Frearson
M
,
Thondan
M
,
Stepto
N
,
Teede
HJ
.
Dietary composition in the treatment of polycystic ovary syndrome: a systematic review to inform evidence-based guidelines
.
J Acad Nutr Diet
2013
a;
113
:
520
–
45
.

Google Scholar

Crossref
Search ADS
PubMed

 

Moran
LJ
,
Lombard
C
,
Noakes
M
.
Is there a need for Clinical Practice Guidelines for the dietary treatment of women with polycystic ovary syndrome
.
Nutr Diet
2009
;
66
:
249
–
251
.

Google Scholar

Crossref
Search ADS

 

Moran
LJ
,
Misso
ML
,
Wild
RA
,
Norman
RJ
.
Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis
.
Hum Reprod Update
2010
b;
16
:
347
–
363
.

Google Scholar

Crossref
Search ADS
PubMed

 

Moran
LJ
,
Ranasinha
S
,
Zoungas
S
,
McNaughton
SA
,
Brown
WJ
,
Teede
HJ
.
The contribution of diet, physical activity and sedentary behaviour to body mass index in women with and without polycystic ovary syndrome
.
Hum Reprod
2013
b;
28
:
2276
–
2283
.

Google Scholar

Crossref
Search ADS
PubMed

 

Naessen
S
,
Carlstrom
K
,
Garoff
L
,
Glant
R
,
Hirschberg
AL
.
Polycystic ovary syndrome in bulimic women--an evaluation based on the new diagnostic criteria
.
Gynecol Endocrinol
2006
;
22
:
388
–
394
.

Google Scholar

Crossref
Search ADS
PubMed

 

Neumark-Sztainer
D
,
French
SA
,
Jeffery
RW
.
Dieting for weight loss: associations with nutrient intake among women
.
J Am Diet Assoc
1996
;
96
:
1172
–
1175
.

Google Scholar

Crossref
Search ADS
PubMed

 

Neumark-Sztainer
D
,
Rock
CL
,
Thornquist
MD
,
Cheskin
LJ
,
Neuhouser
ML
,
Barnett
MJ
.
Weight-control behaviors among adults and adolescents: associations with dietary intake
.
Prev Med
2000
;
30
:
381
–
391
.

Google Scholar

Crossref
Search ADS
PubMed

 

Noakes
M
,
Keogh
JB
,
Foster
PR
,
Clifton
PM
.
Effect of an energy-restricted, high-protein, low-fat diet relative to a conventional high-carbohydrate, low-fat diet on weight loss, body composition, nutritional status, and markers of cardiovascular health in obese women
.
Am J Clin Nutr
2005
;
81
:
1298
–
1306
.

Google Scholar

PubMed
OpenURL Placeholder Text

 

Ossolinski
G
,
Jiwa
M
,
McManus
A
.
Weight management practices and evidence for weight loss through primary care: a brief review
.
Curr Med Res Opin
2015
;
31
:
1
–
25
.

Google Scholar

Crossref
Search ADS

 

Schwingshackl
L
,
Hoffmann
G
.
Long-term effects of low glycemic index/load vs. high glycemic index/load diets on parameters of obesity and obesity-associated risks: a systematic review and meta-analysis
.
Nutr Metab Cardiovasc Dis
2013
;
23
:
699
–
706
.

Google Scholar

Crossref
Search ADS
PubMed

 

Sharma
A
,
Walker
DM
,
Atiomo
W
.
National survey on management of weight reduction in PCOS women in the United Kingdom
.
Eur J Obstet Gynecol Reprod Biol
2010
;
152
:
181
–
185
.

Google Scholar

Crossref
Search ADS
PubMed

 

Stepto
NK
,
Cassar
S
,
Joham
AE
,
Hutchison
SK
,
Harrison
CL
,
Goldstein
RF
,
Teede
HJ
.
Women with polycystic ovary syndrome have intrinsic insulin resistance on euglycaemic-hyperinsulaemic clamp
.
Hum Reprod
2013
;
28
:
777
–
784
.

Google Scholar

Crossref
Search ADS
PubMed

 

Teede
HJ
,
Joham
AE
,
Paul
E
,
Moran
LJ
,
Loxton
D
,
Jolley
D
,
Lombard
C
.
Longitudinal weight gain in women identified with polycystic ovary syndrome: results of an observational study in young women
.
Obesity
2013
;
21
:
1526
–
1532
.

Google Scholar

Crossref
Search ADS
PubMed

 

Teede
HJ
,
Misso
ML
,
Deeks
AA
,
Moran
LJ
,
Stuckey
BG
,
Wong
JL
,
Norman
RJ
,
Costello
MF
.
Assessment and management of polycystic ovary syndrome: summary of an evidence-based guideline
.
Med J Aust
2011
;
195
:
S65
–
112
.

Google Scholar

Crossref
Search ADS
PubMed

 

Thomas
DE
,
Elliott
EJ
,
Baur
L
.
Low glycaemic index or low glycaemic load diets for overweight and obesity
.
Cochrane Database Syst Rev
2007
:
CD005105
.

Google Scholar

OpenURL Placeholder Text

 

Timperio
A
,
Cameron-Smith
D
,
Burns
C
,
Crawford
D
.
The public's response to the obesity epidemic in Australia: weight concerns and weight control practices of men and women
.
Public Health Nutr
2000
;
3
:
417
–
424
.

Google Scholar

Crossref
Search ADS
PubMed

 

Wright
CE
,
Zborowski
JV
,
Talbott
EO
,
McHugh-Pemu
K
,
Youk
A
.
Dietary intake, physical activity, and obesity in women with polycystic ovary syndrome
.
Int J Obes Relat Metab Disord
2004
;
28
:
1026
–
1032
.

Google Scholar

Crossref
Search ADS
PubMed

 
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